Identification of a Two-Gene (PML-EPB41) Signature With Independent Prognostic Value in Osteosarcoma

Abstract

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Background: Osteosarcoma (OSA) is the most prevalent form of malignant bone cancer and it occurs predominantly in children and adolescents. OSA is associated with a poor prognosis and highest cause of cancer-related death. However, there are a few biomarkers that can serve as reasonable assessments of prognosis.Methods: Gene expression profiling data were downloaded from dataset GSE39058 and GSE21257 from the Gene Expression Omnibus database as well as TARGET database. Bioinformatic analysis with data integration was conducted to discover the significant biomarkers for predicting prognosis. Verification was conducted by qPCR and western blot to measure the expression of genes.Results: 733 seed genes were selected by combining the results of the expression profiling data with hub nodes in a human protein-protein interaction network with their gene functional enrichment categories identified. Following by Cox proportional risk regression modeling, a 2-gene (PML-EPB41) signature was developed for prognostic prediction of patients with OSA. Patients in the high-risk group had significantly poorer survival outcomes than in the low-risk group. Finally, the signature was validated and analyzed by the external dataset along with Kaplan–Meier survival analysis as well as biological experiment. A molecular gene model was built to serve as an innovative predictor of prognosis for patients with OSA.Conclusion: Our findings define novel biomarkers for OSA prognosis, which will possibly aid in the discovery of novel therapeutic targets with clinical applications.

Keywords

• osteosarcoma
• protein-protein interaction network
• gene signature
• prognostic prediction
• survival analysis